{"id":1274,"date":"2023-07-27T01:24:14","date_gmt":"2023-07-27T01:24:14","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%ae-dfbetas\/"},"modified":"2023-07-27T01:24:14","modified_gmt":"2023-07-27T01:24:14","slug":"r-%e3%81%ae-dfbetas","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%ae-dfbetas\/","title":{"rendered":"R \u3067 dfbetas \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u7d71\u8a08\u3067\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u89b3\u6e2c\u5024\u304c<\/a>\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3069\u306e\u3088\u3046\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3092\u77e5\u308a\u305f\u3044\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u89b3\u6e2c\u5024\u306e\u5f71\u97ff\u3092\u8a08\u7b97\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001 DFBETAS<\/strong>\u3068\u3057\u3066\u77e5\u3089\u308c\u308b\u30e1\u30c8\u30ea\u30c3\u30af\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u500b\u3005\u306e\u89b3\u6e2c\u5024\u3092\u9664\u53bb\u3059\u308b\u3053\u3068\u306b\u3088\u308b\u5404\u4fc2\u6570\u306b\u5bfe\u3059\u308b\u6a19\u6e96\u5316\u3055\u308c\u305f\u52b9\u679c\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30e1\u30c8\u30ea\u30af\u30b9\u306f\u3001\u7279\u5b9a\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u5404\u4fc2\u6570\u63a8\u5b9a\u306b\u5bfe\u3059\u308b\u5404\u89b3\u6e2c\u5024\u306e\u5f71\u97ff\u306b\u3064\u3044\u3066\u306e\u30a2\u30a4\u30c7\u30a2\u3092\u4e0e\u3048\u3066\u304f\u308c\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u30e2\u30c7\u30eb\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e DFBETAS \u3092\u8a08\u7b97\u3057\u3066\u8996\u899a\u5316\u3059\u308b\u65b9\u6cd5\u3092\u6bb5\u968e\u7684\u306b\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30b9\u30c6\u30c3\u30d7 1: \u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n

\u307e\u305a\u3001R \u306b\u7d44\u307f\u8fbc\u307e\u308c\u3066\u3044\u308bmtcars<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #fit a regression model<\/span>\nmodel <- lm(mpg~disp+hp, data=mtcars)\n\n#view model summary\n<\/span>summary(model)\n\nCoefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 30.735904 1.331566 23.083 < 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.127 on 29 degrees of freedom\nMultiple R-squared: 0.7482, Adjusted R-squared: 0.7309 \nF-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-09\n<\/strong><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 2: \u5404\u89b3\u6e2c\u5024\u306e DFBETAS \u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306b\u3001\u7d44\u307f\u8fbc\u307f\u306edfbetas()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e DFBETAS \u5024\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #calculate DFBETAS for each observation in the model\n<\/span>dfbetas <- as<\/span> . data<\/span> . frame<\/span> (dfbetas(model))\n\n#display DFBETAS for each observation\n<\/span>dfbetas\n\n                      (Intercept) disp hp\nMazda RX4 -0.1174171253 0.030760632 1.748143e-02\nMazda RX4 Wag -0.1174171253 0.030760632 1.748143e-02\nDatsun 710 -0.1694989349 0.086630144 -3.332781e-05\nHornet 4 Drive 0.0577309674 0.078971334 -8.705488e-02\nHornet Sportabout -0.0204333878 0.237526523 -1.366155e-01\nValiant -0.1711908285 -0.139135639 1.829038e-01\nDuster 360 -0.0312338677 -0.005356209 3.581378e-02\nMerc 240D -0.0312259577 -0.010409922 2.433256e-02\nMerc 230 -0.0865872595 0.016428917 2.287867e-02\nMerc 280 -0.1560683502 0.078667906 -1.911180e-02\nMerc 280C -0.2254489597 0.113639937 -2.760800e-02\nMerc 450SE 0.0022844093 0.002966155 -2.855985e-02\nMerc 450SL 0.0009062022 0.001176644 -1.132941e-02\nMerc 450SLC 0.0041566755 0.005397169 -5.196706e-02\nCadillac Fleetwood 0.0388832216 -0.134511133 7.277283e-02\nLincoln Continental 0.0483781688 -0.121146607 5.326220e-02\nChrysler Imperial -0.1645266331 0.236634429 -3.917771e-02\nFiat 128 0.5720358325 -0.181104179 -1.265475e-01\nHonda Civic 0.3490872162 -0.053660545 -1.326422e-01\nToyota Corolla 0.7367058819 -0.268512348 -1.342384e-01\nToyota Corona -0.2181110386 0.101336902 5.945352e-03\nDodge Challenger -0.0270169005 -0.123610713 9.441241e-02\nAMC Javelin -0.0406785103 -0.141711468 1.074514e-01\nCamaro Z28 0.0390139262 0.012846225 -5.031588e-02\nPontiac Firebird -0.0549059340 0.574544346 -3.689584e-01\nFiat X1-9 0.0565157245 -0.017751582 -1.262221e-02\nPorsche 914-2 0.0839169111 -0.028670987 -1.240452e-02\nLotus Europa 0.3444562478 -0.402678927 2.135224e-01\nFord Pantera L -0.1598854695 -0.094184733 2.320845e-01\nFerrari Dino -0.0343997122 0.248642444 -2.344154e-01\nMaserati Bora -0.3436265545 -0.511285637 7.319066e-01\nVolvo 142E -0.1784974091 0.132692956 -4.433915e-02\n<\/strong><\/pre>\n
\u5404\u89b3\u6e2c\u5024\u306b\u3064\u3044\u3066\u3001\u305d\u306e\u7279\u5b9a\u306e\u89b3\u6e2c\u5024\u3092\u524a\u9664\u3057\u305f\u3068\u304d\u306b\u767a\u751f\u3059\u308b\u539f\u70b9\u3001 disp<\/em>\u5909\u6570\u3001\u304a\u3088\u3073hp<\/em>\u5909\u6570\u306e\u4fc2\u6570\u63a8\u5b9a\u5024\u306e\u5dee\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u4e00\u822c\u306b\u3001\u89b3\u6e2c\u5024\u306e DBETAS \u5024\u304c\u3057\u304d\u3044\u5024 2\/\u221a n<\/span> ( n<\/em>\u306f\u89b3\u6e2c\u5024\u306e\u6570) \u3088\u308a\u5927\u304d\u3044\u5834\u5408\u3001\u89b3\u6e2c\u5024\u306f\u7279\u5b9a\u306e\u4fc2\u6570\u306e\u63a8\u5b9a\u306b\u5f37\u3044\u5f71\u97ff\u3092\u4e0e\u3048\u3066\u3044\u308b\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u4f8b\u3067\u306f\u3001\u3057\u304d\u3044\u5024\u306f0.3535534<\/strong>\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n
 #find number of observations<\/span>\nn <- nrow<\/span> (mtcars)\n\n#calculate DFBETAS threshold value<\/span>\nthresh <- 2\/ sqrt<\/span> (n)\n\nthresh\n\n[1] 0.3535534\n<\/strong><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 3: DFBETAS \u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/p>\n
\u6700\u5f8c\u306b\u3001\u30e2\u30c7\u30eb\u5185\u306e\u5404\u89b3\u6e2c\u5024\u3068\u5404\u4e88\u6e2c\u5b50\u306e DFBETAS \u5024\u3092\u8996\u899a\u5316\u3059\u308b\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 #specify 2 rows and 1 column in plotting region<\/span>\nby(mfrow=c(2,1))\n\n#plot DFBETAS for disp<\/em> with threshold lines<\/span>\nplot(dfbetas$disp, type=' h<\/span> ')\nabline(h = thresh, lty = 2)\nabline(h = -thresh, lty = 2)\n\n#plot DFBETAS for hp<\/em> with threshold lines<\/span> \nplot(dfbetas$hp, type=' h<\/span> ')\nabline(h = thresh, lty = 2)\nabline(h = -thresh, lty = 2)\n<\/strong><\/pre>\n
\"R<\/p>\n
\u5404\u30d7\u30ed\u30c3\u30c8\u3067\u306f\u3001X \u8ef8\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u8868\u793a\u3057\u3001Y \u5024\u306f\u5404\u89b3\u6e2c\u5024\u3068\u5404\u4e88\u6e2c\u5b50\u306e\u5bfe\u5fdc\u3059\u308b DFBETAS \u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6700\u521d\u306e\u30d7\u30ed\u30c3\u30c8\u3067\u306f\u30013 \u3064\u306e\u89b3\u6e2c\u5024\u304c\u7d76\u5bfe\u3057\u304d\u3044\u50240.3535534<\/strong>\u3092\u8d85\u3048\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u30012 \u756a\u76ee\u306e\u30d7\u30ed\u30c3\u30c8\u3067\u306f\u30012 \u3064\u306e\u89b3\u6e2c\u5024\u304c\u7d76\u5bfe\u3057\u304d\u3044\u5024\u3092\u8d85\u3048\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u3089\u306e\u89b3\u6e2c\u5024\u304c\u30e2\u30c7\u30eb\u4fc2\u6570\u306e\u63a8\u5b9a\u306b\u4e0d\u5f53\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u3066\u3044\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u3001\u3053\u308c\u3089\u306e\u89b3\u6e2c\u5024\u3092\u3088\u308a\u8a73\u3057\u304f\u8abf\u67fb\u3059\u308b\u3053\u3068\u3092\u9078\u629e\u3059\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
R \u3067\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a>
R \u3067\u91cd\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a>
R \u3067\u30ec\u30d0\u30ec\u30c3\u30b8\u7d71\u8a08\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a>
R \u3067 DFFITS \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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